Data distribution system, method and program product

ABSTRACT

A data distribution system, method and a computer program product therefor. Computers provisioned with operations centers supporting individual locations share resources with organizations in multiple locations. Each operations center receives and evaluates local information for the supported location and selectively provides evaluated information for reuse by other locations. A data exchange agent in each operations center publishes information available from a supported location to a publication subscription unit. The operations center also subscribes to the publication subscription unit for information available from other locations. The publication subscription unit identifies matches between subscriptions and publications. A negotiation unit negotiates matched information transfers between operations centers.

CROSS REFERENCE TO RELATED APPLICATION

The present invention is a related to U.S. patent application Ser. No.13/751,856 (Attorney Docket No. YOR920120601US1), “DATA DISTRIBUTIONSYSTEM, METHOD AND PROGRAM PRODUCT” to Marcos Dias De Assuncao et al.,filed Jan. 28, 2013, assigned to the assignee of the present inventionand incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is related to sharing locally generated data amongorganizations in other locations and more particularly to moreefficiently distribute data collected/generated for one location withother locations that may otherwise be unaware of, but that may have aneed or use for, the data.

2. Background Description

A typical broad geographic area may cover many smaller locations, eachmanaged and serviced by local authorities, e.g., organizations,government departments, and individuals. Local authorities are settingup operation centers, such as the IBM Intelligent Operations Center, toefficiently monitor and manage services for the location, e.g., police,fire departments, traffic management and weather. See, e.g.,www-01.ibm.com/software/industry/intelligent-oper-center/.

A state of the art operation center includes an emergency capabilitythat facilitates proactively addressing local emergencies. Inparticular, the operation center emergency capability facilitatesdepartments in generating, collecting, and processing voluminousinformation about the local environment from a wide range of locationservices and simulation engines. Sources of this information include,for example, police departments, fire departments, traffic managementsystems, weather forecasts, and flooding simulation. The usefulness ofmuch of this data produced, processed and collected by one entity mayoverlap with, be common with, and frequently is relevant to, not onlyother local organizations, but also to organizations in one or more ofthe other (e.g., surrounding) localities.

A typical operation center normally simulates and models localconditions and extreme weather conditions, e.g., traffic, weather andflooding in metropolitan areas. By combining local sensor data with thesimulation results the operation center can identify possibleinfrastructure disruptions. After using the simulation results toidentify potential disruptions, the operation center can identifysimilar conditions as they arise, and trigger appropriate localresponses, e.g., initiate processes to circumvent and/or minimizeeffects of the disruptions. Thus, the simulation and model results havemade an operation center an important tool in minimizing the impact offlooding and, moreover, for flood prevention planning in highlypopulated areas. Similarly, a typical operation center uses simulationand model data to facilitate situational planning for dry regions, e.g.,to mitigate bush fire damage to crops.

A complete data picture is important to analyzing and predicting thepotential impact of extreme or hazardous conditions for a specificlocale. While, a typical simulation may focus on a small, limited area,the results generally depend on data from a more widespread region andsurroundings. Simulating extreme weather conditions, for example, ahurricane impacting on a city, requires data from surrounding areas, andeven distant locations. Locating and identifying all relevant data thatmay be available, has not been a simple task.

Thus, there is a need for discovering available geographically specificdata and in particular for facilitating allowing owners ofgeographically specific data to share costs, and optimize producing andusing geographically specific data.

SUMMARY OF THE INVENTION

A feature of the invention is more efficient sharing/distribution ofdata collected/generated by an organization with and among, otherorganizations that may be interested in the data;

Another feature of the invention is proactively distribution ofcollected/generated data in mutually agreeable format;

Yet another feature of the invention is more efficient distribution ofcollected/generated data, sharing the data with organizations indifferent locales, in a format suitable to other organizations.

The present invention relates to a data distribution system, method anda computer program product therefor. Computers provisioned withoperations centers supporting individual locations share resources withorganizations in multiple locations. Each operations center receives andevaluates local information for the supported location and selectivelyprovides evaluated information for reuse by other locations. A dataexchange agent in each operations center publishes information availablefrom a supported location to a publication subscription unit. Theoperations center also subscribes to the publication subscription unitfor information available from other locations. The publicationsubscription unit identifies matches between subscriptions andpublications. A negotiation unit negotiates matched informationtransfers between operations centers.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects and advantages will be betterunderstood from the following detailed description of a preferredembodiment of the invention with reference to the drawings, in which:

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention;

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention;

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention;

FIG. 4A shows an example of a preferred system distributing and sharingdata between organizations servicing neighboring locales according to apreferred embodiment of the present invention;

FIG. 4B shows a detailed example of a typical locale, e.g., a city, andcity organizations;

FIG. 5 shows an example of multiple preferred operation centers inshared information technology (IT) infrastructure (e.g., in cloudcomputing infrastructure) facilitating data consumption and production;

FIGS. 6A-B show an example of collecting and publishing data 180, andsubscribing and receiving published data;

FIG. 7 shows an example of negotiations with the originating operationcenter providing potential customer operation centers with data samplesfor quality testing.

DESCRIPTION OF PREFERRED EMBODIMENTS

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed and as further indicated hereinbelow.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing 68; transactionprocessing; and data sharing 70.

FIG. 4A shows an example of a preferred system 100 distributing andsharing data between organizations servicing neighboring locales 102,104, 106, 108, according to a preferred embodiment of the presentinvention. Organizations servicing neighboring locales 102, 104, 106,108, clients of one or more system computers 110, 112, 114 connected tonetwork 116, share geographically specific data, collected or generatedand stored, e.g., in local storage 34 or in network attached storage(NAS) 118. Preferably, system computers 110, 112, 114 are cloud 100computers, each provisioned with an operations center 120, 122 orcenters supporting organizations in one or more locations 102, 104, 106,108. Locale 102, 104, 106, 108 inhabitants, organizations (public andprivate) and individuals, produce data that is specific to a particulargeographic region, i.e., the respective locale 102, 104, 106, 108.

FIG. 4B shows a detailed example of a typical locale 104, e.g., a city,and local organizations 130, 132, 134, 136, 138, 140 in the city 104. Inthis example the organizations include utilities 130, public safety 132,law enforcement 134, administration infrastructure 136, 138 and weatherservice 140. The utilities 130 may include, for example, water andpower. Public safety 132 may include, for example, fire/emergencyresponse. Law enforcement 134 may include police, for example.Administration infrastructure may include traffic condition monitoring136 and medical/hospitals 138. Weather service 140 typically monitorsflooding and other emergency and non-emergency weather events.

The utilities 130, public safety 132 and law enforcement 134 stream data142, 144, 146 over the network 116 to the operations center 120. Thestreamed data 142, 144, 146 can originate, for example, from sensors andsystems monitoring local infrastructure and services. Similarly, theadministration infrastructure 136, 138 and weather service 140 eachmaintain local data 148, 150 that is accessible over the network 116 bythe operations center 120. Thus, the weather service 140 may collect andlocally maintain data 148, 150 from weather forecast and environmentalsimulations. The operations center 120 includes a data exchange agent152 consuming collected data 142, 144, 146, 148, 150, selectivelydistributing data 154, 156 and selectively receiving data from otherlocations.

Thus, the data exchange agent 152 may run simulations on data collectedfor consumption locally, monitor location conditions and select data forexternal distribution, e.g., on an available data stream 154 orotherwise make collected data available 156 to organizations forservices in other locations. Examples of services that may consumeavailable data include, for example, forecast precipitation data forflood simulations in a given geographical area, e.g., 122, 124.Simultaneously, the data exchange agent 152 may collect available datafrom other locations made available by other data exchange agents 152for other locations.

A preferred system 100 selects data 142, 144, 146, 148, 150 collectedfor the local organizations 130, 132, 134, 136, 138, 140 in one locale,e.g., 104, to make available 154, 156 for reuse by other organizationsin other locales 102, 106, 108, and conditions for such reuse. Forexample, re-use/distribution conditions can include what resolution tooffer, parameter ranges, and measurement sampling frequency. Also otherorganizations may use a preferred system 100 to specify interestrequirements to match in available data and data streams. Once a matchis found, organizations can renegotiate data transactions usingfiltering and aggregation techniques based on data sampling. Inparticular, local organizations 130, 132, 134, 136, 138, 140 can exploredata samples to determine whether the data quality is likely to besatisfactory and/or whether to adjust data requirements. Thus,advantageously, the present invention reduces data production costs, andprovides data collecting/generating organizations 130, 132, 134, 136,138, 140 with business opportunities for marketing the data.

FIG. 5 shows an example of multiple preferred operation centers 120, 122in shared information technology (IT) infrastructure (e.g., in cloudcomputing infrastructure) facilitating data production and consumptionaccording to a preferred embodiment of the present invention withreference to FIGS. 4A-B, identical features being labeled identically.In this example, local organizations in one locale, e.g., 104, generatethe data from services or simulations and local organizations in anotherlocale, e.g., 106, which may be a potential data consumer. So, thepreferred operation center 120 makes generated data available topotential data consumer organizations through another preferredoperation center, e.g., 122, such that potential data consumptionorganization can select generated data that meet local reuse needs.

Thus in this example, the generating operation center 120 also includesa preferences and conditions unit 160, a data publication unit 162 and afilter/aggregations setup or generation unit 164, which generates anddeploys filter/aggregators 166 for handling incoming data streams 142,144, 146. Similarly, in addition to a data exchange agent 168, theconsumption operation center 122 includes a requirements definition unit170 and a data subscription unit 172. A publication subscription unit174 matches published data with subscribed interests of potentialconsumers. A parameter negotiation unit 176 negotiates and renegotiatesdata transactions between the generating operation center 120 and theconsumption operation center 122. The setup or generation unit 164,deployed filter/aggregator 166 and parameter negotiation unit 176cooperate as a data exchange adjustment component set.

Preferably, the publication subscription unit 174 is a content-basedpublish/subscribe service, e.g., data analytics processing 68 in FIG. 3,allowing the operation centers 122 to subscribe to streams 154 and data156 provided (or published) by other organizations 120. The operationcenters 120, 122 define publications and subscriptions according tometadata properties that contain the characteristics of available dataand/or a certain desired stream or data. Thus, the data publication unit162 defines the preferences as a set of n attribute-value pairs.Likewise, the data subscription unit 172 defines subscriptioncharacteristics as a set of n attribute-value pairs. Preferably, eachattribute a_i includes a name and a value v_i with a comparison operator(e.g., ≦, <, ≧, >, _=, =). The attribute values may be, for example,numerical, or an alphanumeric character string. Further, the datasubscription unit 172 may specify allowed values as single values or asranges of values. Attributes defining characteristics may include, forexample, offered resolution, parameter ranges and measurement samplingfrequency. Accordingly, published data or a published stream with acertain set of characteristics matches a subscription, if and only ifpublished attributes satisfy all subscription requirement attributes.

It should be noted that although shown herein as organizations in locale104 generating data for consumption in locale 106, this is for exampleonly. Typically, additional location organizations may be generatingdata and, as the need/opportunity arises, consuming data from eachother. So, while consuming data from organizations in location 104,organizations in location 106 may be generating data for consumption byorganizations in locations 102, 104 and 108 with preferred operationcenters 120, 122 managing distribution as described for transactionsjust between organizations in location 104 and location 106.

FIG. 6A shows an example of collecting and publishing data 180, and FIG.6B shows an example of subscribing and receiving published data 200.First, the data exchange agent, 152 in the example of FIG. 5,receives/collects data 182 from sources 142, 144, 146, 148, 150 and uses184 the data for simulations or analysis to generate data streams 154 orfiles 156 for use. The data collecting/generating organizations 130,132, 134, 136, 138, 140, take appropriate action 186 to handle localincidents, e.g., in infrastructure and services. The preferences andconditions unit 160 specifies conditions 188 for making input and outputstreams 142, 144, 146, 154 and collected data 148, 150, 156 available toother organizations for reuse. The data publication unit 162 translates190 the specified conditions into characteristics and parameter ranges,and the publication subscription unit 174 publishes 192 the results.

So in one example, the operation center 120 may have precipitationforecast (attribute or characteristics type) data 150 with a minimumresolution of 1 Km² for the locale 104. The operation center 120 maydecide to make this data available, publishing 192 to other locations102, 106, 108 at the same or a lower resolution, e.g., within 1 Km² and15 Km². In another example, the operation center 120 may have trafficsimulation results (characteristics type=“traffic”) within a 100 Km²geographical area. The operation center 120 may publish 192 the resultsabout certain geographical areas in that larger area 100 Km² or with a 1Km²<resolution<50 Km² and for a 50 Km²<geographical area<150 Km² forthat locale 104.

Subsequently, the data exchange agent 152 for another operation center122 identifies 202 a local need for data in FIG. 6B. The requirementsdefinition unit 170 defines local data requirements 204 and the datasubscription unit 172 subscribes 206 with publication subscription unit174 for certain preferences of streams or for acquiring streamed datathat satisfy defined local data requirements. The operation centers 120,122 share a data sample and negotiate 208 data characteristics forreuse. When the publication subscription unit 174 matches 210 offerconditions 192 with search preferences 204 for the search. The setupunit 164 sets up the filter(s) and/or aggregator(s) 214 required tomanipulate or re-format the data for the requesting operation center122. The deployed filter/aggregator 166 modifies and/or manipulates thedata 216 to meet the negotiated data characteristics. If the provideddata fails to satisfy the needs for the subscribing operation center122, the centers 120, 122 may enter data adjustment 212. Data adjustment212 begins a re-negotiation 212, where the organization centers 120, 122re-negotiate different data characteristics for reuse based on updatedrequirements.

So in the above traffic example, operation center 122 subscribes withdata type=“traffic,” 5 Km²<resolution<10 Km², geographical area=100 Km²and location=locale 106. The published 192 characteristics fromoperation center 120 fall in the subscription ranges and match 210. Forn operation centers, a preferred publication subscription unit 174 ofFIG. 5 selects one organization with the maximum requirements (r₁, r₂,r₃, . . . , r_(n)) matching, sharing or in common with, published datacharacteristics (c₁, c₂, c₃, . . . , c_(n)) as a match. Preferably, therequested characteristics fall in the range of the providedcharacteristics. So for example, even if the publishing operation center120 publishes data with an original resolution range of 5 Km² to 15 Km²,the offered/published resolution may be 1 km² during negotiation 208 ifthe subscribing operation center 122 may requests a 1 km² resolution.After negotiation 208 the publishing operation center 120 dynamicallygenerates 214 and applies filters and aggregators 216 during delivery ofthe data files 142, 144, 146 and streams 148, 150.

FIG. 7 shows an example of negotiations 208 with potential customers(other subscribing operation centers, e.g., 122), wherein theoriginating operation center, 102 in this example, provides potentialcustomers with data samples for quality testing. The subscribingoperation center 122 first initializes 2080 a data quality variable,e.g., set the variable to 0. Then, the subscribing operation center(s)122 begins iteratively examining the data 2082 until quality exceeds apreviously defined, expected quality, or if the data parameters remainwithin an offered range.

So first, the subscribing operation center(s) 122 sends a request 2084for a data sample and assesses sample quality 2086. If the quality doesnot exceed, or match expected quality, the data does not match and thesubscribing operation center(s) 122 continues sending requests 2084.Once the quality exceeds or matches 2088 expected quality, thesubscribing operation center 122 checks whether the sample indicatesthat data requirements need to be adjusted 2090, e.g., for higher/lowerresolution. If the sample indicates that the data fails to meetrequirements, the sample does not match and the subscribing operationcenter 122 sends a request 2084 to begin the next iteration. Otherwise,the data matches 210 and the subscribing operation center 122 downloadsthe data 2100.

Thereafter, the subscribing operation center 122 may decide to refinecharacteristics during data adjustment 212, which begins after thepublication subscription unit 174 finds a match 210. Preferably duringdata adjustment 212, filtering and aggregation 216 selects data thatmaximize intersecting published/subscribed parameter ranges for eachdata attribute. While using downloaded data, the subscribing operationcenter 122 may discover that the previously negotiatedcharacteristics/requirements do not produce desired results, e.g., bychecking the simulation results. If the data produces inadequateresults, the operations center 120, 122 may renegotiate 210 thecharacteristics, and the publishing operations center 120 dynamicallyadjusts filters and aggregators to the new agreement. So in the aboveexample, the characteristics may be re-negotiated 210 for a newresolution, e.g., 2 km² and the filter/aggregator adjusted to the new 2km² resolution. Of course, an organization may cancel at any pointduring negotiation or re-negotiation.

Aggregators and filters each may be in software and may be used both totransfer samples and for downloading. A typical aggregator operates ondata from two or more sources (e.g., 142, 144, 146, 148, 150 in FIG. 4B)to produce one or more outputs 154, 156. Typical examples of aggregatoroperations include, but are not limited to, averaging values,calculating the standard deviation and otherwise performing dataanalysis on multiple values. A typical filter acts on data from sources(e.g., 142, 144, 146, 148, 150) to selectively remove portions accordingto previously specified filtering criteria, e.g., reducing resolution orother attributes, sampling and/or anonymizing the data.

An economics mechanism may be used to compensate publishing operationcenters for the data. For an example of a suitable economics mechanismsee, U.S. patent application Ser. No. 13/751,856 (Attorney Docket No.YOR920120601US1), “DATA DISTRIBUTION SYSTEM, METHOD AND PROGRAM PRODUCT”to Marcos Dias De Assuncao et al., filed Jan. 28, 2013, assigned to theassignee of the present invention and incorporated herein by reference.

Thus advantageously, the present invention allows organizations toselectively make collected data available for reuse by otherorganizations, local or remote, and further, to specify preferences forsuch reuse, e.g., at an offered resolution, with selected parameterranges and at a particular sampling frequency. The present inventionalso allows other, potential consuming organizations to specify interestin available data and data streams to find requirement matches. Further,the present invention facilitates adjusting to meet changingrequirements through data negotiation and renegotiation using filteringand aggregation based on data sampling.

While the invention has been described in terms of preferredembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theappended claims. It is intended that all such variations andmodifications fall within the scope of the appended claims. Examples anddrawings are, accordingly, to be regarded as illustrative rather thanrestrictive.

What is claimed is:
 1. A data distribution system comprising: one ormore computers sharing resources with organizations in a plurality oflocations; at least two operations centers, each supporting one of saidplurality of locations and provisioned in said one or more computers,each operations center receiving and evaluating local information forthe supported location and selectively providing evaluated informationfor reuse by other locations; a publication subscription unit; a dataexchange agent in said each operations center, said data exchange agentpublishing to said publication subscription unit information availablefrom a respective supported location and subscribing to said publicationsubscription unit information available from other locations, saidpublication subscription unit identifying subscriptions for informationmatching published information; and a negotiation unit negotiatingmatched information transfers with said at least two operations centers.2. A data distribution system as in claim 1, wherein said localinformation is data streamed from local sensors and collected by systemsmonitoring local infrastructure and services for local organizationspassing said data to the respective operations center, said respectiveoperation center selectively making evaluated information available. 3.A data distribution system as in claim 2, wherein any said operationscenter with information for publication includes a preferences andconditions unit and a data publication unit.
 4. A data distributionsystem as in claim 3, wherein said preferences and conditions unitspecifies conditions for making input and output streams and collecteddata for availability to other organizations for reuse and said datapublication unit translates specified publication conditions intocharacteristics and parameter ranges.
 5. A data distribution system asin claim 1, wherein any said operations center subscribing forinformation includes a requirements definition unit and a datasubscription unit.
 6. A data distribution system as in claim 5, whereinsaid requirements definition unit defines local data requirements andsaid data subscription unit subscribes with publication subscriptionunit.
 7. A data distribution system as in claim 1, wherein said one ormore computers are cloud computers and said publication subscriptionunit and said negotiation unit are provisioned in said cloud computers.8. A data distribution system as in claim 7, wherein at least twooperations centers comprises an operations center for each of saidplurality of locations, said local information is data streamed from,and collected by, local organizations passing said data to therespective operations center, said respective operation centerselectively making evaluated information available to other operationcenters, each said operations center further comprising: a preferencesand conditions unit specifying conditions for making input and outputstreams and collected data for availability to other organizations forreuse; a data publication unit translating specified publicationconditions into characteristics and parameter ranges; a requirementsdefinition unit defining local data requirements; and a datasubscription unit subscribing with publication subscription unit.
 9. Adata distribution system as in claim 1, wherein said local organizationsfor at least one location include utility providers, a public safety andlaw enforcement, location administration and local weather service. 10.A data distribution method comprising: collecting data about a location,said location being one of a plurality of locations, each having one ormore local organizations sharing resources on one or more computers;analyzing collected data for local needs; responding locally responsiveto data analysis; identifying data conditions for making data resultsavailable to other said local organizations; and publishing datacharacteristics of available data with a publication subscription unitresponsive to identified said data conditions.
 11. A data distributionmethod as in claim 10, further comprising: determining a local need fordata; determining data requirements for said local need; subscribingwith said publication subscription unit for available published datamatching said data requirements; and checking said data characteristicsfor published data for a match with said data requirements.
 12. A datadistribution method as in claim 11, further comprising: identifying anysubscription matching said published data; generating afilter/aggregator responsive to said identified data conditions;applying said filter/aggregator to conform said data results to saididentified data conditions; and downloading conforming said data resultsto said location supported by the subscribing operations center.
 13. Adata distribution method as in claim 11, wherein checking said datacharacteristics for a match with said data requirements comprises:requesting a data sample; assessing a data quality based on said sample;checking whether sample quality matches an indicated expected quality;and whenever quality matches determining whether said datacharacteristics match said data requirements, data being downloaded fora matching sample.
 14. A data distribution method as in claim 13,wherein another sample is requested and assessed whenever sample qualityor data characteristics do not match.
 15. A data distribution method asin claim 14, wherein said data characteristics are defined byattributes, said attributes including an offered resolution, parameterranges and a measurement sampling frequency.
 16. A computer programproduct for sharing and distributing location data, said computerprogram product comprising a computer usable medium having computerreadable program code stored thereon, said computer readable programcode comprising: computer readable program code means for an operationscenter supporting organizations in one of a plurality of locations, saidoperations center receiving and evaluating local information for thesupported location and selectively providing evaluated information forreuse by organizations in others of said plurality of locations;computer readable program code means for publishing from a respectivesupported location; computer readable program code means for subscribingto information available from other locations; computer readable programcode means for matching subscriptions for information with matchingpublished information; and computer readable program code means fornegotiating matched information transfers with operations centerssupporting the publishing location and the subscribing location.
 17. Acomputer program product for sharing and distributing location data asin claim 16, wherein said local information is data streamed from, andcollected by, local organizations passing said data to the respectiveoperations center, said respective operation center selectively makingevaluated information available.
 18. A computer program product forsharing and distributing location data as in claim 16, wherein saidcomputer readable program code means for publishing specifies conditionsfor making input and output streams and collected data available toother organizations for reuse and translates specified publicationconditions into characteristics and parameter ranges.
 19. A computerprogram product for sharing and distributing location data among clientsof a cloud as in claim 16, wherein said computer readable program codemeans for subscribing defines local data requirements.
 20. A computerprogram product for location data sharing and distribution as in claim16, wherein said at least two operations centers are an operationscenter for each of said plurality of locations provisioned on cloudbased computers supporting said plurality of locations, said localinformation is data streamed from, and collected by, local organizationspassing said data over a network to the respective operations center inthe cloud, said respective operation center selectively making evaluatedinformation available to other operation centers, each said operationscenter further comprising: computer readable program code means forspecifying conditions for making input and output streams and collecteddata for availability to other organizations for reuse; computerreadable program code means for translating specified publicationconditions into characteristics and parameter ranges; computer readableprogram code means for defining local data requirements; and computerreadable program code means for subscribing with publicationsubscription unit.
 21. A computer program product for location datasharing and distribution, said computer program product comprising acomputer usable medium having computer readable program code storedthereon, said computer readable program code causing a plurality ofcomputers executing said code to: collect data about a location, saidlocation being one of a plurality of locations, each having one or morelocal organizations sharing resources on said plurality of computers;analyze collected data for local needs; respond locally responsive todata analysis; identify data conditions for making data resultsavailable to other said local organizations; and publish datacharacteristics of available data with a publication subscription unitresponsive to identified said data conditions.
 22. A computer programproduct for location data sharing and distribution as in claim 21, saidcomputer readable program code further causing said plurality ofcomputers executing said code to: determine a local need for data;determine data requirements for said local need; subscribe with saidpublication subscription unit for available published data matching saiddata requirements; and check said data characteristics for publisheddata for a match with said data requirements.
 23. A computer programproduct for location data sharing and distribution as in claim 22, saidcomputer readable program code further causing said plurality ofcomputers executing said code to: identify any subscription matchingsaid published data; generate a filter/aggregator responsive to saididentified data conditions; apply said filter/aggregator to conform saiddata results to said identified data conditions; and download conformingsaid data results to said location supported by the subscribingoperations center.
 24. A computer program product for location datasharing and distribution as in claim 22, wherein checking said datacharacteristics for a match with said data requirements causes saidplurality of computers executing said code to: request a data sample;assess a data quality based on said sample; check whether sample qualitymatches an indicated expected quality; and whenever quality matchesdetermine whether said data characteristics match said datarequirements, data being downloaded for a matching sample.
 25. Acomputer program product for location data sharing and distribution asin claim 24, wherein said plurality of computers are a plurality ofcloud computers with provisioned operations centers managing datapublication and subscription, each operation center requesting andassessing another sample whenever sample quality or data characteristicsdo not match, and said data characteristics are defined by attributes,said attributes including an offered resolution, parameter ranges and ameasurement sampling frequency.